{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2b7da21-f761-4b32-9b9e-f8985e2f8dc9",
   "metadata": {},
   "source": [
    "Chapter 19\n",
    "# Pandas常见运算\n",
    "Book_1《编程不难》 | 鸢尾花书：从加减乘除到机器学习  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7fd0dce-2785-492d-8c71-40feddea5500",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "911d90ec-053a-4e21-b476-3a7ec5fa41f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df = sns.load_dataset(\"iris\")\n",
    "# 从Seaborn中导入鸢尾花数据帧"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eddcca5e-d3fd-4123-a4f1-34ad30f03f46",
   "metadata": {},
   "source": [
    "## 加减乘除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8c25e62-ee8e-4f1d-b335-7ce05fd9e21b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\2962866553.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_df_['X1_centered'] = X_df_['X1'] - X_df_['X1'].mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x165f79dbc70>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1617.75x1500 with 27 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the iris data\n",
    "X_df = iris_df.copy()\n",
    "\n",
    "X_df.rename(columns = {'sepal_length':'X1', \n",
    "                       'sepal_width':'X2'}, \n",
    "            inplace = True)\n",
    "\n",
    "X_df_ = X_df[['X1','X2', 'species']]\n",
    "\n",
    "#%% data transformation\n",
    "\n",
    "X_df_['X1 - E(X1)'] = X_df_['X1'] - X_df_['X1'].mean()\n",
    "X_df_['X2 - E(X2)'] = X_df_['X2'] - X_df_['X2'].mean()\n",
    "X_df_['X1 + X2'] = X_df_['X1'] + X_df_['X2'] \n",
    "X_df_['X1 - X2'] = X_df_['X1'] - X_df_['X2'] \n",
    "X_df_['X1 * X2'] = X_df_['X1'] * X_df_['X2'] \n",
    "X_df_['X1 / X2'] = X_df_['X1'] / X_df_['X2'] \n",
    "X_df_.drop(['X1','X2'], axis=1, inplace=True)\n",
    "\n",
    "# 可视化\n",
    "\n",
    "sns.pairplot(X_df_, corner=True, hue=\"species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb1204b2-1f67-47e5-8b6b-890e797d193d",
   "metadata": {},
   "source": [
    "## 统计汇总"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51ab495d-f96b-4f57-807a-e0145fa142de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据帧统计汇总\n",
    "# 注意很多统计运算只针对数值，比如自动忽略'species'一列\n",
    "iris_df.describe()\n",
    "# 参考：\n",
    "# https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8fa6e56c-ea2f-4aa7-ba3c-1586f1397adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150.000000\n",
       "mean       5.843333\n",
       "std        0.828066\n",
       "min        4.300000\n",
       "25%        5.100000\n",
       "50%        5.800000\n",
       "75%        6.400000\n",
       "max        7.900000\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于特定列的汇总\n",
    "iris_df.sepal_length.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d383ad0a-1381-4570-a98c-6dc9c631ddce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150.000000\n",
       "mean       5.843333\n",
       "std        0.828066\n",
       "min        4.300000\n",
       "25%        5.100000\n",
       "50%        5.800000\n",
       "75%        6.400000\n",
       "max        7.900000\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 等价于\n",
    "iris_df['sepal_length'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1884bd-53ed-4a8e-9dca-cfee0cdc7086",
   "metadata": {},
   "source": [
    "### 最大、最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7253dfa7-bf19-4c16-a204-3b9fdcc3b5bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length          7.9\n",
       "sepal_width           4.4\n",
       "petal_length          6.9\n",
       "petal_width           2.5\n",
       "species         virginica\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a799876-b059-42dd-a50d-a25aa71fafd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length       4.3\n",
       "sepal_width        2.0\n",
       "petal_length       1.0\n",
       "petal_width        0.1\n",
       "species         setosa\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d467a732-1c65-4c03-8f37-313e035e0458",
   "metadata": {},
   "source": [
    "### 均值、中位数、众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "664acacf-18e2-45eb-844d-2049b57d09cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\2873841849.py:2: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sepal_length    5.843333\n",
       "sepal_width     3.057333\n",
       "petal_length    3.758000\n",
       "petal_width     1.199333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 均值\n",
    "iris_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5aaa1e0e-63de-4370-904c-50447abe4926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\210965164.py:2: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sepal_length    5.80\n",
       "sepal_width     3.00\n",
       "petal_length    4.35\n",
       "petal_width     1.30\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 中位数\n",
    "iris_df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff0262f3-a40d-44b1-8640-ada859d319cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.0          3.0           1.4          0.2\n",
       "1           NaN          NaN           1.5          NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 众数\n",
    "iris_df.mode(numeric_only = True, dropna=True)\n",
    "# ???"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e48ac2a-1bcd-4f7e-849e-a5d35c58d82b",
   "metadata": {},
   "source": [
    "### 独特值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "86b2bd20-841e-4edf-b5f1-46fc0c17f27d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 鸢尾花种类 'species' 一列独特值\n",
    "iris_df['species'].unique()\n",
    "# 常用来获得标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a901577-f3d8-4f7b-8d49-9190f396de94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['setosa', 'versicolor', 'virginica']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数组转化为列\n",
    "iris_df['species'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "850a7fe8-3024-4109-8b13-2732a98d8278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 鸢尾花种类 'species' 独特值数量\n",
    "iris_df['species'].nunique()\n",
    "# 常用来获得标签数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc29e8c9-1893-40ba-aacd-c6d3be10a58e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bf75fd5d-7f08-4d22-b77d-7f134ec6e07a",
   "metadata": {},
   "source": [
    "### 排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45533d2f-2446-4653-80e4-654a33304ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "13            4.3          3.0           1.1          0.1     setosa\n",
       "42            4.4          3.2           1.3          0.2     setosa\n",
       "38            4.4          3.0           1.3          0.2     setosa\n",
       "8             4.4          2.9           1.4          0.2     setosa\n",
       "41            4.5          2.3           1.3          0.3     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "122           7.7          2.8           6.7          2.0  virginica\n",
       "118           7.7          2.6           6.9          2.3  virginica\n",
       "117           7.7          3.8           6.7          2.2  virginica\n",
       "135           7.7          3.0           6.1          2.3  virginica\n",
       "131           7.9          3.8           6.4          2.0  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据花萼长度排序，默认从小到大\n",
    "iris_df.sort_values(['sepal_length'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8880434b-b001-4089-8882-d4d720e4330c",
   "metadata": {},
   "source": [
    "### 分位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "20484558-9674-4514-a700-788688f28d0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\834856868.py:1: FutureWarning: The default value of numeric_only in DataFrame.quantile is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.quantile([0.05, 0.95])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>4.600</td>\n",
       "      <td>2.345</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>7.255</td>\n",
       "      <td>3.800</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sepal_length  sepal_width  petal_length  petal_width\n",
       "0.05         4.600        2.345           1.3          0.2\n",
       "0.95         7.255        3.800           6.1          2.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.quantile([0.05, 0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e29b4b9-462f-4b0d-8d75-8e2c54a62b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "131           7.9          3.8           6.4          2.0  virginica\n",
       "135           7.7          3.0           6.1          2.3  virginica\n",
       "122           7.7          2.8           6.7          2.0  virginica\n",
       "117           7.7          3.8           6.7          2.2  virginica\n",
       "118           7.7          2.6           6.9          2.3  virginica\n",
       "..            ...          ...           ...          ...        ...\n",
       "41            4.5          2.3           1.3          0.3     setosa\n",
       "42            4.4          3.2           1.3          0.2     setosa\n",
       "38            4.4          3.0           1.3          0.2     setosa\n",
       "8             4.4          2.9           1.4          0.2     setosa\n",
       "13            4.3          3.0           1.1          0.1     setosa\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据花萼长度排序，默认从大到小\n",
    "iris_df.sort_values(['sepal_length'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "899db713-aebf-4a84-8fa5-fae72efbc6a2",
   "metadata": {},
   "source": [
    "### 样本方差\n",
    "注意，分母 n - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f02884a5-3193-46e1-a44a-8a927a024c4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\2829397562.py:1: FutureWarning: The default value of numeric_only in DataFrame.var is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.var()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sepal_length    0.685694\n",
       "sepal_width     0.189979\n",
       "petal_length    3.116278\n",
       "petal_width     0.581006\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46489975-0b46-4c95-ba1a-62d22d52b065",
   "metadata": {},
   "source": [
    "### 样本标准差\n",
    "注意，分母 n - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c27a749b-5e73-4100-8cec-72e04c01f3b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\18420640.py:1: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.std()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sepal_length    0.828066\n",
       "sepal_width     0.435866\n",
       "petal_length    1.765298\n",
       "petal_width     0.762238\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599983a4-81d3-4173-b75c-fc025e939879",
   "metadata": {},
   "source": [
    "### 协方差矩阵\n",
    "注意，默认分母n - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2aca8db-1e6e-42b6-bf3b-0b461f30bdd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\3314449492.py:1: FutureWarning: The default value of numeric_only in DataFrame.cov is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.cov()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.685694</td>\n",
       "      <td>-0.042434</td>\n",
       "      <td>1.274315</td>\n",
       "      <td>0.516271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.042434</td>\n",
       "      <td>0.189979</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>-0.121639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>1.274315</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>3.116278</td>\n",
       "      <td>1.295609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.516271</td>\n",
       "      <td>-0.121639</td>\n",
       "      <td>1.295609</td>\n",
       "      <td>0.581006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      0.685694    -0.042434      1.274315     0.516271\n",
       "sepal_width      -0.042434     0.189979     -0.329656    -0.121639\n",
       "petal_length      1.274315    -0.329656      3.116278     1.295609\n",
       "petal_width       0.516271    -0.121639      1.295609     0.581006"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e569f899-463c-416e-b3a0-513f2261937f",
   "metadata": {},
   "source": [
    "### 相关性系数矩阵\n",
    "三种类型{‘pearson’, ‘kendall’, ‘spearman’}，默认pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63e5ceef-d2aa-484e-bf0a-a4961fec4bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\1934569051.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.corr()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9a851bc-8d6d-4e13-ae7f-2d5c558740e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\276201852.py:1: FutureWarning: The default value of numeric_only in DataFrame.skew is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.skew()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sepal_length    0.314911\n",
       "sepal_width     0.318966\n",
       "petal_length   -0.274884\n",
       "petal_width    -0.102967\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "26d40e58-fa65-4561-9d58-d7c670505355",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\3298255430.py:1: FutureWarning: The default value of numeric_only in DataFrame.kurt is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.kurt()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sepal_length   -0.552064\n",
       "sepal_width     0.228249\n",
       "petal_length   -1.402103\n",
       "petal_width    -1.340604\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.kurt()\n",
    "# 也可以用 iris_df.kurtosis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c67c43c-4050-48c7-b023-9d3da3aa75eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eed04aee-1799-4ff6-83e6-f77185a8ff28",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1e37dba-591d-40bf-9a4d-d34e709fdd6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f38d56ce-3588-4152-ae19-f9f9109bad86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             6.0          4.0           2.0          1.0\n",
       "1             5.0          3.0           2.0          1.0\n",
       "2             5.0          4.0           2.0          1.0\n",
       "3             5.0          4.0           2.0          1.0\n",
       "4             5.0          4.0           2.0          1.0\n",
       "..            ...          ...           ...          ...\n",
       "145           7.0          3.0           6.0          3.0\n",
       "146           7.0          3.0           5.0          2.0\n",
       "147           7.0          3.0           6.0          2.0\n",
       "148           7.0          4.0           6.0          3.0\n",
       "149           6.0          3.0           6.0          2.0\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "iris_df_rounded = np.ceil(iris_df[iris_df.columns[:4]])\n",
    "# 向上取整\n",
    "iris_df_rounded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a827c916-b010-4b72-8c89-b35be3e9e7f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6., 5., 7., 8.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df_rounded.sepal_length.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f84f4252-b4df-4ebd-b5fe-6fa7f51eb7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4., 3., 5., 2.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df_rounded.sepal_width.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e60906a5-6f39-4cbd-996c-5873988e946d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length  sepal_width  petal_length  petal_width\n",
       "5.0           4.0          2.0           1.0            19\n",
       "6.0           4.0          2.0           1.0            19\n",
       "7.0           3.0          5.0           2.0            17\n",
       "6.0           3.0          5.0           2.0            15\n",
       "7.0           4.0          6.0           3.0            11\n",
       "6.0           3.0          4.0           2.0             8\n",
       "5.0           3.0          2.0           1.0             8\n",
       "7.0           3.0          6.0           2.0             7\n",
       "              4.0          5.0           2.0             6\n",
       "              3.0          6.0           3.0             5\n",
       "6.0           3.0          6.0           2.0             4\n",
       "              5.0          2.0           1.0             3\n",
       "8.0           3.0          7.0           3.0             3\n",
       "                                         2.0             3\n",
       "6.0           3.0          4.0           1.0             3\n",
       "7.0           4.0          6.0           2.0             2\n",
       "8.0           4.0          7.0           3.0             2\n",
       "6.0           4.0          5.0           2.0             2\n",
       "5.0           3.0          4.0           1.0             2\n",
       "7.0           3.0          4.0           2.0             1\n",
       "6.0           3.0          6.0           3.0             1\n",
       "                           5.0           1.0             1\n",
       "                           3.0           2.0             1\n",
       "5.0           4.0          1.0           1.0             1\n",
       "8.0           3.0          6.0           2.0             1\n",
       "                                         3.0             1\n",
       "5.0           3.0          5.0           2.0             1\n",
       "8.0           4.0          6.0           2.0             1\n",
       "                           7.0           2.0             1\n",
       "5.0           2.0          4.0           1.0             1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df_rounded.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9b0bf96e-1d53-495d-bd32-fb81b953b649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length  sepal_width\n",
       "6.0           3.0            33\n",
       "7.0           3.0            30\n",
       "6.0           4.0            21\n",
       "5.0           4.0            20\n",
       "7.0           4.0            19\n",
       "5.0           3.0            11\n",
       "8.0           3.0             8\n",
       "              4.0             4\n",
       "6.0           5.0             3\n",
       "5.0           2.0             1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df_rounded[['sepal_length','sepal_width']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "217a0df8-a2e6-4e84-adc7-76ce2dd490ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\3023332425.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iris_sepal['count'] = 1\n"
     ]
    }
   ],
   "source": [
    "iris_sepal = iris_df_rounded[['sepal_length','sepal_width']]\n",
    "iris_sepal['count'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c4eb7a6-3800-4b4c-8c4c-16e654dfaa12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "31f2c111-831b-4267-9105-2d46c74ad058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count                 \n",
       "sepal_length   5.0   6.0   7.0  8.0\n",
       "sepal_width                        \n",
       "2.0            1.0   NaN   NaN  NaN\n",
       "3.0           11.0  33.0  30.0  8.0\n",
       "4.0           20.0  21.0  19.0  4.0\n",
       "5.0            NaN   3.0   NaN  NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency_matrix =iris_sepal.groupby(['sepal_length','sepal_width']).count().unstack(level=0)\n",
    "frequency_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ce452bc0-552e-4f01-b853-e1386eaf5208",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 将连续转为分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7b754450-520f-4b67-9f9b-d99c58da695a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       short\n",
       "1       short\n",
       "2       short\n",
       "3       short\n",
       "4       short\n",
       "        ...  \n",
       "145    medium\n",
       "146    medium\n",
       "147    medium\n",
       "148    medium\n",
       "149    medium\n",
       "Name: sepal_length, Length: 150, dtype: category\n",
       "Categories (3, object): ['short' < 'medium' < 'long']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(iris_df.sepal_length, bins = 3, labels = ['short', 'medium', 'long'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd7bb97-70fb-4ae1-a38b-6971eaab57d8",
   "metadata": {},
   "source": [
    "## groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5e19a433-c07a-4974-90f3-593917743a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             5.006        3.428         1.462        0.246\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df = sns.load_dataset(\"iris\")\n",
    "iris_df.groupby(['species']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4b238825-e734-4382-a2f8-1df5f3308fb5",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>0.352490</td>\n",
       "      <td>0.379064</td>\n",
       "      <td>0.173664</td>\n",
       "      <td>0.105386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>0.516171</td>\n",
       "      <td>0.313798</td>\n",
       "      <td>0.469911</td>\n",
       "      <td>0.197753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>0.635880</td>\n",
       "      <td>0.322497</td>\n",
       "      <td>0.551895</td>\n",
       "      <td>0.274650</td>\n",
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      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa          0.352490     0.379064      0.173664     0.105386\n",
       "versicolor      0.516171     0.313798      0.469911     0.197753\n",
       "virginica       0.635880     0.322497      0.551895     0.274650"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.groupby(['species']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3aa08e5c-59f3-4f0e-ba95-ec10b0b084bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sepal_length</th>\n",
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       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
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       "      <th>setosa</th>\n",
       "      <td>0.124249</td>\n",
       "      <td>0.143690</td>\n",
       "      <td>0.030159</td>\n",
       "      <td>0.011106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>0.266433</td>\n",
       "      <td>0.098469</td>\n",
       "      <td>0.220816</td>\n",
       "      <td>0.039106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>0.404343</td>\n",
       "      <td>0.104004</td>\n",
       "      <td>0.304588</td>\n",
       "      <td>0.075433</td>\n",
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      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa          0.124249     0.143690      0.030159     0.011106\n",
       "versicolor      0.266433     0.098469      0.220816     0.039106\n",
       "virginica       0.404343     0.104004      0.304588     0.075433"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.groupby(['species']).var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3538b8d8-bb46-4d27-a2a1-5507f4bce78c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.016355</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>0.030159</td>\n",
       "      <td>0.006069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.010331</td>\n",
       "      <td>0.009298</td>\n",
       "      <td>0.006069</td>\n",
       "      <td>0.011106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">versicolor</th>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.266433</td>\n",
       "      <td>0.085184</td>\n",
       "      <td>0.182898</td>\n",
       "      <td>0.055780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>0.085184</td>\n",
       "      <td>0.098469</td>\n",
       "      <td>0.082653</td>\n",
       "      <td>0.041204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.182898</td>\n",
       "      <td>0.082653</td>\n",
       "      <td>0.220816</td>\n",
       "      <td>0.073102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.055780</td>\n",
       "      <td>0.041204</td>\n",
       "      <td>0.073102</td>\n",
       "      <td>0.039106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">virginica</th>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.404343</td>\n",
       "      <td>0.093763</td>\n",
       "      <td>0.303290</td>\n",
       "      <td>0.049094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>0.093763</td>\n",
       "      <td>0.104004</td>\n",
       "      <td>0.071380</td>\n",
       "      <td>0.047629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.303290</td>\n",
       "      <td>0.071380</td>\n",
       "      <td>0.304588</td>\n",
       "      <td>0.048824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.049094</td>\n",
       "      <td>0.047629</td>\n",
       "      <td>0.048824</td>\n",
       "      <td>0.075433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                                      \n",
       "setosa     sepal_length      0.124249     0.099216      0.016355     0.010331\n",
       "           sepal_width       0.099216     0.143690      0.011698     0.009298\n",
       "           petal_length      0.016355     0.011698      0.030159     0.006069\n",
       "           petal_width       0.010331     0.009298      0.006069     0.011106\n",
       "versicolor sepal_length      0.266433     0.085184      0.182898     0.055780\n",
       "           sepal_width       0.085184     0.098469      0.082653     0.041204\n",
       "           petal_length      0.182898     0.082653      0.220816     0.073102\n",
       "           petal_width       0.055780     0.041204      0.073102     0.039106\n",
       "virginica  sepal_length      0.404343     0.093763      0.303290     0.049094\n",
       "           sepal_width       0.093763     0.104004      0.071380     0.047629\n",
       "           petal_length      0.303290     0.071380      0.304588     0.048824\n",
       "           petal_width       0.049094     0.047629      0.048824     0.075433"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics = iris_df.groupby(['species']).cov()\n",
    "three_cov_matrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "67878cc0-b96d-467a-b234-c28ec6f4e26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(    'setosa', 'sepal_length'),\n",
       "            (    'setosa',  'sepal_width'),\n",
       "            (    'setosa', 'petal_length'),\n",
       "            (    'setosa',  'petal_width'),\n",
       "            ('versicolor', 'sepal_length'),\n",
       "            ('versicolor',  'sepal_width'),\n",
       "            ('versicolor', 'petal_length'),\n",
       "            ('versicolor',  'petal_width'),\n",
       "            ( 'virginica', 'sepal_length'),\n",
       "            ( 'virginica',  'sepal_width'),\n",
       "            ( 'virginica', 'petal_length'),\n",
       "            ( 'virginica',  'petal_width')],\n",
       "           names=['species', None])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ca73c53b-de69-40c4-a3ed-21252b3d711e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics.columns.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "44b225f1-d24e-4105-b6d5-2e0d934e7a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['setosa', 'versicolor', 'virginica']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics.index.get_level_values(0).unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ba5c8c84-3daa-462b-8b3a-185abdfb64c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['setosa', 'versicolor', 'virginica']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics.index.get_level_values('species').unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c4ffe116-3fcf-488c-94df-c0ce93ccca0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics.index.get_level_values(1).unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "32c8baac-b2b5-4a6c-86c4-319f8945c25b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
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       "      <th>petal_width</th>\n",
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      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      0.124249     0.099216      0.016355     0.010331\n",
       "sepal_width       0.099216     0.143690      0.011698     0.009298\n",
       "petal_length      0.016355     0.011698      0.030159     0.006069\n",
       "petal_width       0.010331     0.009298      0.006069     0.011106"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics.loc['setosa']\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d93bf2f-e2ce-4eec-8f7e-7315c8b617f6",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      0.124249     0.099216      0.016355     0.010331\n",
       "sepal_width       0.099216     0.143690      0.011698     0.009298\n",
       "petal_length      0.016355     0.011698      0.030159     0.006069\n",
       "petal_width       0.010331     0.009298      0.006069     0.011106"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_cov_matrics.xs('setosa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "536ab132-6590-4f36-8805-f68791ac85e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\james\\AppData\\Local\\Temp\\ipykernel_12332\\2721001069.py:1: FutureWarning: The default value of numeric_only in DataFrame.cov is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  iris_df.loc[iris_df['species'] == 'setosa'].cov()\n"
     ]
    },
    {
     "data": {
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       "      <th>sepal_length</th>\n",
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       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.124249</td>\n",
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       "      <td>0.016355</td>\n",
       "      <td>0.010331</td>\n",
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       "    <tr>\n",
       "      <th>sepal_width</th>\n",
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       "      <th>petal_length</th>\n",
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       "    <tr>\n",
       "      <th>petal_width</th>\n",
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      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      0.124249     0.099216      0.016355     0.010331\n",
       "sepal_width       0.099216     0.143690      0.011698     0.009298\n",
       "petal_length      0.016355     0.011698      0.030159     0.006069\n",
       "petal_width       0.010331     0.009298      0.006069     0.011106"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.loc[iris_df['species'] == 'setosa'].cov()\n",
    "# 介绍条件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3518af3c-0e94-43b7-8298-1000218382e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f41d85-fd21-49b9-9a37-f307e6a34301",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "98e44597-059f-482c-81ca-855a4bda8c55",
   "metadata": {},
   "source": [
    "## agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "014c4093-11e8-446a-998a-2152c3e6648a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>876.500000</td>\n",
       "      <td>458.600000</td>\n",
       "      <td>563.700000</td>\n",
       "      <td>179.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>0.685694</td>\n",
       "      <td>0.189979</td>\n",
       "      <td>3.116278</td>\n",
       "      <td>0.581006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sepal_length  sepal_width  petal_length  petal_width\n",
       "sum     876.500000   458.600000    563.700000   179.900000\n",
       "min       4.300000     2.000000      1.000000     0.100000\n",
       "max       7.900000     4.400000      6.900000     2.500000\n",
       "std       0.828066     0.435866      1.765298     0.762238\n",
       "var       0.685694     0.189979      3.116278     0.581006\n",
       "mean      5.843333     3.057333      3.758000     1.199333\n",
       "mean      5.843333     3.057333      3.758000     1.199333"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.iloc[:,0:4].agg(['sum', 'min', 'max', 'std', 'var', 'mean', np.mean])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0174068a-fd54-45da-ae12-641faddc89db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
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       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.855622</td>\n",
       "      <td>3.443333</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.3</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>3.070000</td>\n",
       "      <td>3.100000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.2</td>\n",
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       "      <td>1.703917</td>\n",
       "      <td>2.903333</td>\n",
       "      <td>3.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.550269</td>\n",
       "      <td>2.403333</td>\n",
       "      <td>3.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.814754</td>\n",
       "      <td>3.293333</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>145</th>\n",
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       "      <td>1.861003</td>\n",
       "      <td>3.463333</td>\n",
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       "      <td>13.8</td>\n",
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       "      <td>4.600000</td>\n",
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       "      <td>1.769181</td>\n",
       "      <td>3.130000</td>\n",
       "      <td>4.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.442221</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.497776</td>\n",
       "      <td>2.243333</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sum  min  max       std       var      mean\n",
       "0    10.0  1.4  5.1  1.855622  3.443333  3.333333\n",
       "1     9.3  1.4  4.9  1.752142  3.070000  3.100000\n",
       "2     9.2  1.3  4.7  1.703917  2.903333  3.066667\n",
       "3     9.2  1.5  4.6  1.550269  2.403333  3.066667\n",
       "4    10.0  1.4  5.0  1.814754  3.293333  3.333333\n",
       "..    ...  ...  ...       ...       ...       ...\n",
       "145  14.9  3.0  6.7  1.861003  3.463333  4.966667\n",
       "146  13.8  2.5  6.3  1.931321  3.730000  4.600000\n",
       "147  14.7  3.0  6.5  1.769181  3.130000  4.900000\n",
       "148  15.0  3.4  6.2  1.442221  2.080000  5.000000\n",
       "149  14.0  3.0  5.9  1.497776  2.243333  4.666667\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.iloc[:,0:3].agg(['sum', 'min', 'max', 'std', 'var', 'mean'], axis = 'columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad44b7b5-6a71-446e-84c9-3e73a7aa0b92",
   "metadata": {},
   "source": [
    "## map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "adf60b8c-d360-43cc-9740-a955a82d5532",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# 加载鸢尾花数据集\n",
    "iris = sns.load_dataset('iris')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5f77c74d-4e0f-4b7c-94bf-97a04f62279b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
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       "      <th>species</th>\n",
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       "      <td>setosa</td>\n",
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       "      <td>setosa</td>\n",
       "      <td>短</td>\n",
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       "      <th>4</th>\n",
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       "      <td>setosa</td>\n",
       "      <td>中等</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>长</td>\n",
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       "      <td>virginica</td>\n",
       "      <td>长</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>长</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>中等</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "0             5.1          3.5           1.4          0.2     setosa   \n",
       "1             4.9          3.0           1.4          0.2     setosa   \n",
       "2             4.7          3.2           1.3          0.2     setosa   \n",
       "3             4.6          3.1           1.5          0.2     setosa   \n",
       "4             5.0          3.6           1.4          0.2     setosa   \n",
       "..            ...          ...           ...          ...        ...   \n",
       "145           6.7          3.0           5.2          2.3  virginica   \n",
       "146           6.3          2.5           5.0          1.9  virginica   \n",
       "147           6.5          3.0           5.2          2.0  virginica   \n",
       "148           6.2          3.4           5.4          2.3  virginica   \n",
       "149           5.9          3.0           5.1          1.8  virginica   \n",
       "\n",
       "    sepal_length_category  \n",
       "0                      中等  \n",
       "1                       短  \n",
       "2                       短  \n",
       "3                       短  \n",
       "4                      中等  \n",
       "..                    ...  \n",
       "145                     长  \n",
       "146                     长  \n",
       "147                     长  \n",
       "148                     长  \n",
       "149                    中等  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义映射函数\n",
    "def map_sepal_length(sepal_length):\n",
    "    if sepal_length < 5:\n",
    "        return \"短\"\n",
    "    elif 5 <= sepal_length < 6:\n",
    "        return \"中等\"\n",
    "    else:\n",
    "        return \"长\"\n",
    "\n",
    "# 使用 map 方法将花萼长度映射为分类值\n",
    "iris['sepal_length_category'] = iris['sepal_length'].map(map_sepal_length)\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "218e080e-a97a-4eb8-9185-1eb80449fe73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "setosa        0.246\n",
       "versicolor    1.326\n",
       "virginica     2.026\n",
       "Name: petal_width, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算鸢尾花各类花瓣平均宽度\n",
    "mean_X2_by_species = iris_df.groupby(\n",
    "    'species')['petal_width'].mean()\n",
    "mean_X2_by_species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9e72231-4c3a-4276-9a28-e9dc80a967b6",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'row' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 10\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNO\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# 使用 map 方法将花瓣宽度映射为是否超过平均值\u001b[39;00m\n\u001b[0;32m      9\u001b[0m iris_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreater_than_mean\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m iris_df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m---> 10\u001b[0m     map_petal_width(\u001b[43mrow\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpetal_width\u001b[39m\u001b[38;5;124m'\u001b[39m], \n\u001b[0;32m     11\u001b[0m                     row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspecies\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     13\u001b[0m iris_df\n",
      "\u001b[1;31mNameError\u001b[0m: name 'row' is not defined"
     ]
    }
   ],
   "source": [
    "# 定义映射函数\n",
    "def map_petal_width(petal_width, species):\n",
    "    if petal_width > mean_X2_by_species[species]:\n",
    "        return \"YES\"\n",
    "    else:\n",
    "        return \"NO\"\n",
    "\n",
    "# 使用 map 方法将花瓣宽度映射为是否超过平均值\n",
    "iris_df['greater_than_mean'] = iris_df.apply(lambda \n",
    "       row: map_petal_width(row['petal_width'], \n",
    "                            row['species']), axis=1)\n",
    "\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "385775f8-d251-4428-bf7f-372cdef986d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6d70b647-a160-47d9-9587-3028ae70c8c2",
   "metadata": {},
   "source": [
    "## apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8503e1e-adde-4247-b2ed-9460392e2ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "iris_df = sns.load_dataset(\"iris\")\n",
    "# 从Seaborn中导入鸢尾花数据帧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "67d98823-6e1f-4d05-974e-89a4472fbc64",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>B</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>B</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>B</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>B</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>C</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species category  \\\n",
       "0             5.1          3.5           1.4          0.2     setosa        C   \n",
       "1             4.9          3.0           1.4          0.2     setosa        D   \n",
       "2             4.7          3.2           1.3          0.2     setosa        D   \n",
       "3             4.6          3.1           1.5          0.2     setosa        D   \n",
       "4             5.0          3.6           1.4          0.2     setosa        C   \n",
       "..            ...          ...           ...          ...        ...      ...   \n",
       "145           6.7          3.0           5.2          2.3  virginica        B   \n",
       "146           6.3          2.5           5.0          1.9  virginica        B   \n",
       "147           6.5          3.0           5.2          2.0  virginica        B   \n",
       "148           6.2          3.4           5.4          2.3  virginica        B   \n",
       "149           5.9          3.0           5.1          1.8  virginica        C   \n",
       "\n",
       "    greater_than_mean  \n",
       "0                  NO  \n",
       "1                  NO  \n",
       "2                  NO  \n",
       "3                  NO  \n",
       "4                  NO  \n",
       "..                ...  \n",
       "145               YES  \n",
       "146                NO  \n",
       "147                NO  \n",
       "148               YES  \n",
       "149                NO  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义函数将花萼长度映射为等级\n",
    "def sepal_length_to_category(sepal_length):\n",
    "    if sepal_length < 5:\n",
    "        return 'D'\n",
    "    elif 5 <= sepal_length < 6:\n",
    "        return 'C'\n",
    "    elif 6 <= sepal_length < 7:\n",
    "        return 'B'\n",
    "    else:\n",
    "        return 'A'\n",
    "\n",
    "# 使用 apply 函数将 sepal_length 映射为等级并添加新列\n",
    "iris_df['category'] = iris_df['sepal_length'].apply(sepal_length_to_category)\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "acca9ef5-0d10-4bf7-a84b-804c22f7234b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "setosa        4.3\n",
       "versicolor    4.9\n",
       "virginica     4.9\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用apply和lambda函数计算每个类别中最小的花瓣宽度\n",
    "iris_df.groupby('species')['sepal_length'].apply(\n",
    "    lambda x: x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38a925b8-c5f5-4845-a25f-f871624c8e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "setosa        4.3\n",
       "versicolor    4.9\n",
       "virginica     4.9\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.groupby('species')['sepal_length'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2a76d332-aee9-4e69-b1d7-20b5eac340a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repo: https://github.com/Visualize-ML\n",
    "# Book 1 Python for Beginners  |  From Basic Arithmetic to Machine Learning\n",
    "# Published and copyrighted by Tsinghua University Press\n",
    "# Beijing, China, 2023"
   ]
  }
 ],
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